Reading through the new vLLM release and it gives ordinary LLMs a very Jev-like trick. 👀 🎯 vLLM 0.32.0 adds a new structured-decision API. Normally, if you want structured output, an LLM might generate prompt → { → key → : → value → JSON... Now with /v1/systemone, you give it a fixed set of legal answers and vLLM reads the model's first-token probabilities. It can return: 🎯 choice → select among allowed answers + probabilities ✅ yes/no → probability of each 📊 score → probabilities across ordered levels And this works with supported ordinary generative models. 👈 The vLLM docs demonstrate it with Qwen3-0.6B Even better, with prefix caching, if you're asking multiple questions about the same state, vLLM can prefill that state once and reuse it. Each additional decision is question → one-token read → probabilities 🔗 vLLM 0.32.0 + structured-decision docs in ALT.